Analysing the relationship between countries’ freedom level and the number of Covid-19 cases

Yıl 2024, , 350 - 357, 30.09.2024

Ümit Çıraklı , Merve Nur Alpaslan

https://doi.org/10.52880/sagakaderg.1461073

Öz

ABSTRACT

Aim: Epidemic diseases have been encountered in many periods of history. Societies took precautions against the epidemic diseases they encountered, according to the science of their time. This study aims to reveal whether the effects of the Covid-19 pandemic vary according to the freedom status of countries.
Materials and Methods: In this study, multiple methods were used with a phased approach to reveal the relationship between the freedom index of countries and Covid-19 cases. In the study, firstly, cluster analysis was performed on the countries. Then, One-Way Variance and Kruskal Wallis analyses were applied to test whether there were differences between Covid-19 cases according to the freedom index groups of the countries, and the analyses were performed with the SPSS 22 program. Finally, the effect of the state of freedom on Covid-19 cases was investigated using panel data analysis, which is an econometric method. The start of the data set is April 1, 2021. The end of the data set was chosen as April 2, 2022. Eviews 9.5 statistical program was used for panel data analysis.
Result: The study concluded that vaccines generally have a reducing effect on the number of deaths from Covid 19. To conclude in more detail, while the number of vaccinations in non-free countries increased, the number of deaths decreased. While the number of vaccinations in free countries is less than in free countries, the decrease in deaths is less than in non-free countries.

Anahtar Kelimeler

World freedom ındex, Covid-19, PMG ARDL, Freedom

Ülkelerin özgürlük düzeyi ile Covid-19 vaka sayısı arasındaki ilişkinin incelenmesi

Öz

ÖZ
Amaç: Tarihin birçok döneminde salgın hastalıklarla karşılaşılmıştır. Toplumlar karşılaştıkları salgın hastalıklarda kendi döneminin bilimine göre önlemler almıştır. Bu çalışmanın amacı Covid-19 pandemisinin etkilerini, ülkelerin özgürlük durumlarına göre değişip değişmediğini ortaya koymaktır.
Gereç ve Yöntem: Bu çalışmada, ülkelerin özgürlük endeksi ile Covid-19 vakaları arasındaki ilişkiyi ortaya koymak için aşamalı bir yaklaşımla birden fazla yöntem kullanılmıştır. Çalışmada ilk olarak, ülkelere kümeleme analizi yapılmıştır. Daha sonra ülkelerin özgürlük endeksi gruplarına göre Covid-19 vakaları arasında farklılık olup olmadığını test etmek için Tek Yönlü Varyans ve Kruskal Wallis analizleri uygulanmıştır, analizler SPSS 22 programıyla yapılmıştır. Son olarak da ekonometrik bir yöntem olan panel veri analizi ile özgürlük durumunun Covid-19 vakaları üzerinde etkisi araştırılmıştır. Veri setinin başlangıcı 1 Nisan 2021 yılıdır. Veri setinin bitişi ise 2 Nisan 2022 yılı olarak seçilmiştir. Panel veri analizi için Eviews 9.5 istatistik programı kullanılmıştır.
Sonuç: Çalışmada genel olarak aşıların covid 19 ölüm sayılarında azaltıcı etkisi olduğu sonucuna ulaşılmıştır. Daha detaylı sonuçlandırılacak olunursa da özgür olmayan ülkelerin aşı olma sayısı yükselirken ölüm sayısı düşmüştür. Özgür ülkelerde aşı olma sayısı özgür olmayan ülkelere göre daha azken ölümdeki düşüş oranı özgür olmayan ülkelere göre daha azdır.

Anahtar Kelimeler

Dünya özgürlük indeksi, Covid-19, PMG ARDL, Özgürlük

Kaynakça

• Asteriou, D. & Hall, S.G. (2007). Applied Econometrics: A Modern Approach Using Eviews and Microfit Revisited Edition, Palgrave Macmillan, Newyork.
• Ayvaz Güven, E. T. & Ayvaz, Y. Y. (2016). The relationship between inflation and unemployment in Turkey: time series analysis. KSU Journal of Social Sciences,13(1), 241-262.
• Balcı, A. (2020). The effects of epidemics on education in Covid-19. International Journal of Leadership Studies: Theory And Practice, 3(3), 75-85.
• Baltagi, B.H. (2005) Econometric Analysis of Panel Data. 3rd Edition, John Wiley & Sons Inc., New York.
• Breuer, J. B., McNown, R., & Wallace, M. (2002). Series‐specific unit root tests with panel data. Oxford Bulletin of Economics and statistics, 64(5), 527-546. doi:10.1111/1468-0084.00276
• Çiçek, İ. , Tanhan, A., Tanrıverdi, S. (2020). Covid-19 and education. Journal of National Education, Education in Turkey and the World in the Pandemic Process, 49(1) 1091-1104. DOI: 10.37669/milliegitim.787736
• Çiftci, F. (2009). The effects of capital flows towards developing countries on economic growth in the process of globalisation: The case of Turkey. (Unpublished Master's Thesis) Muğla University, Institute of Social Sciences, Muğla.
• Çoban, M. N. (2020). Romer hipotezi kapsamında ticari dışa açıklık ve enflasyon ilişkisi: 11 ülkeleri için Panel ARDL analizi. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 11 (3), 651-660. https://dergipark.org.tr/en/pub/gumus/issue/57505/647844
• Daniel, S. J. (2020). La educación y la pandemia COVID-19. Perspectivas, 49, 91-96.
• Dinno A. Nonparametric pairwise multiple comparisons in independent groups using Dunn's test. Stat J 2015; 15: 292-300
• Fonchamnyo, D.C., Dinga, G.D. & Ngum, V.C. (2021). Revisiting the nexus between domestic investment, foreign direct investment and external debt in SSA countries: PMG‐ARDL approach. African Development Review, African Development Bank, (3) 479-491 Doi:10.1111/1467-8268.12593
• Freedom House Access Date: 17.05.2023 https://freedomhouse.org/countries/freedom-world/scores
• Genç, Ö. (2010). The black death: The plague of 1348 and its effects on medieval Europe. Journal of History School, X, 123-150
• Göktaş, Ö. (2005). Teorik ve Uygulamalı Zaman Serileri Analizi. (1. Baskı) İstanbul. Beşir Kitapevi,
• Greyling, T. & Rossouw, S. (2022). Positive attitudes towards COVID-19 vaccines: A cross-country analysis. PloSone peaper, 17 (3),
• Gujarati, D. (2003). Basic Econometrics. 4th ed. New York: McGraw Hill, pp. 638-640.
• Guliyev, H. (2020). Determining the spatial effects of COVID-19 using the spatial panel data model. Spatial statistics, 38)1-10
• Güler, A. ve Özyurt, H. (2011). Merkez bankası bağımsızlığı ve reel ekonomik performans: Panel ARDL analizi. Ekonomi Bilimleri Dergisi, 3 (2), 11-20. Retrieved from https://dergipark.org.tr/tr/pub/ebd/issue/4858/66820
• Güngörer, F. (2020). The Effect of Covid-19 on Social Institutions. Journal of Yüzüncü Yıl University Institute of Social Sciences, Special Issue on Epidemic Diseases , 393-328
• Hsiao, C. (2007). Panel data analysis advantages and challenges. Invited Paper, 16(1), 1–22. doi:10.1007/s11749-007-0046-x
• İnce, U. & Sayın, F. (2022). Current practices in Covid-19 vaccine. Journal of Health Sciences, 31 (2) , 258-262.
• Kalaycı, S. (2010). SPSS Uygulamalı Çok Değişkenli İstatistiksel Teknikler (5. basım). Ankara: Asil Yayıncılık
• Kao, C. (1999) Spurious Regression and Residual-Based Tests for Cointegration in Panel Data. Journal of Econometrics, 90, 1-44.
• Kaufman, L., Rousseeuw, P.J. (1990). Finding groups in data: An introduction to cluster analysis, New York: John Wiley and Sons.
• Köse, E., Oturak, G., Ekerbiçer, H. Ç., Arsan, A., Özaygın, A., Nas, B., & Albishari, S. (2022). Examination of the descriptive characteristics of randomised controlled trials on COVID-19 vaccines. Van Medical Journal, 29(1), 76-83.
• Lee, R.C.T. (1981). Clustering analysis and its applications. In: Tou, J.T. (eds) Advances in Information Systems Science. Boston, MA: Springer.
• Mathieu, E., Ritchie, H., Ortiz-Ospina, E., Roser, M., Hasell, J., Appel, C. & Rodés-Guirao, L. (2021). A global database of COVID-19 vaccinations. Nature human behaviour, 5(7), 947-953.
• McCoskey, S. K., & Selden, T. M. (1998). Health care expenditures and GDP: panel data unit root test results. Journal of Health Economics, 17(3), 369-376.
• Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670.
• Pesaran, M.H., Shin, Y. & Smith, R.P. (1999). Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association, 94(446), 621.
• Pradhan, D., Biswasroy, P., Naik, P.K., Ghosh, G. & Rath, G. (2020). A review of existing interventions to prevent COVID-19. Archives of Medical Research, 51 (5), 363-374)
• Seetaram, N. & Petit, S. (2012). Panel data analysis. In L. Dwyer, A. Gill, & N. Seetaram (Eds.), Handbook of research methods in tourism: Quantitative and qualitative approaches 127-144. Edward Elgar Publishing Ltd.
• Tarkar, P. (2020). Impact of COVID-19 pandemic on education system. International Journal of Advanced Science and Technology, 29(9), 3812-3814.
• Wang, Y.S. (2009). The impact of crisis events and macroeconomic activity on taiwan's international inbound tourism demand. Tourism Management, 30, 75-82.
• WHO. (2020). Coronavirus disease (COVID-19): situation report, 198. World Health Organization. https://apps.who.int/iris/handle/10665/333735